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Data Protection Law and Multi-Party Computation: Applications to Information Exchange between Law Enforcement Agencies

Treiber, Amos ; Müllmann, Dirk ; Schneider, Thomas ; Spiecker genannt Döhmann, Indra (2022)
Data Protection Law and Multi-Party Computation: Applications to Information Exchange between Law Enforcement Agencies.
CCS '22: 2022 ACM SIGSAC Conference on Computer and Communications Security. Los Angeles, USA (07.11.2022)
doi: 10.1145/3559613.3563192
Konferenzveröffentlichung, Bibliographie

Kurzbeschreibung (Abstract)

Pushes for increased power of Law Enforcement (LE) for data retention and centralized storage result in legal challenges with data protection law and courts—and possible violations of the right to privacy. This is motivated by a desire for better cooperation and exchange between LE Agencies (LEAs), which is difficult due to data protection regulations, was identified as a main factor of major public security failures, and is a frequent criticism of LE. Secure Multi-Party Computation (MPC) is often seen as a technological means to solve privacy conflicts where actors want to exchange and analyze data that needs to be protected due to data protection laws. In this interdisciplinary work, we investigate the problem of private information exchange between LEAs from both a legal and technical angle. We give a legal analysis of secret-sharing based MPC techniques in general and, as a particular application scenario, consider the case of matching LE databases for lawful information exchange between LEAs. We propose a system for lawful information exchange between LEAs using MPC and private set intersection and show its feasibility by giving a legal analysis for data protection and a technical analysis for workload complexity. Towards practicality, we present insights from qualitative feedback gathered within exchanges with a major European LEA.

Typ des Eintrags: Konferenzveröffentlichung
Erschienen: 2022
Autor(en): Treiber, Amos ; Müllmann, Dirk ; Schneider, Thomas ; Spiecker genannt Döhmann, Indra
Art des Eintrags: Bibliographie
Titel: Data Protection Law and Multi-Party Computation: Applications to Information Exchange between Law Enforcement Agencies
Sprache: Englisch
Publikationsjahr: 7 November 2022
Verlag: ACM
Buchtitel: WPES'22: Proceedings of the 21st Workshop on Privacy in the Electronic Society
Veranstaltungstitel: CCS '22: 2022 ACM SIGSAC Conference on Computer and Communications Security
Veranstaltungsort: Los Angeles, USA
Veranstaltungsdatum: 07.11.2022
DOI: 10.1145/3559613.3563192
Kurzbeschreibung (Abstract):

Pushes for increased power of Law Enforcement (LE) for data retention and centralized storage result in legal challenges with data protection law and courts—and possible violations of the right to privacy. This is motivated by a desire for better cooperation and exchange between LE Agencies (LEAs), which is difficult due to data protection regulations, was identified as a main factor of major public security failures, and is a frequent criticism of LE. Secure Multi-Party Computation (MPC) is often seen as a technological means to solve privacy conflicts where actors want to exchange and analyze data that needs to be protected due to data protection laws. In this interdisciplinary work, we investigate the problem of private information exchange between LEAs from both a legal and technical angle. We give a legal analysis of secret-sharing based MPC techniques in general and, as a particular application scenario, consider the case of matching LE databases for lawful information exchange between LEAs. We propose a system for lawful information exchange between LEAs using MPC and private set intersection and show its feasibility by giving a legal analysis for data protection and a technical analysis for workload complexity. Towards practicality, we present insights from qualitative feedback gathered within exchanges with a major European LEA.

Fachbereich(e)/-gebiet(e): DFG-Graduiertenkollegs
DFG-Graduiertenkollegs > Graduiertenkolleg 2050 Privacy and Trust for Mobile Users
Hinterlegungsdatum: 15 Nov 2022 14:22
Letzte Änderung: 31 Jan 2023 10:27
PPN: 504188380
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